





"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPImageProcessor, CLIPProcessor




@require_vision
class 					__lowercase      (      unittest.TestCase				):







					'''simple docstring'''




					def 			_lowerCamelCase		(    self  ):
									__a			:    List[Any]      							=	tempfile.mkdtemp()

									# fmt: off
									__a			:    Optional[int]      							=	['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
									# fmt: on
									__a			:    Dict      							=	dict(zip(_UpperCAmelCase						,      range(len(_UpperCAmelCase  )  )  )  )
									__a			:    List[str]      							=	['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
									__a			:    List[str]      							=	{'''unk_token''': '''<unk>'''}

									__a			:    int      							=	os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['''vocab_file''']  )
									__a			:    Tuple      							=	os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['''merges_file''']  )
									with open(self.vocab_file						,      '''w'''						,      encoding='''utf-8'''  ) as fp:
													fp.write(json.dumps(_UpperCAmelCase  ) + '''\n'''  )
									with open(self.merges_file						,      '''w'''						,      encoding='''utf-8'''  ) as fp:
													fp.write('''\n'''.join(_UpperCAmelCase  )  )

									__a			:    Optional[Any]      							=	{
									    '''do_resize''': True,
									    '''size''': 20,
									    '''do_center_crop''': True,
									    '''crop_size''': 18,
									    '''do_normalize''': True,
									    '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
									    '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
									}
									__a			:    List[Any]      							=	os.path.join(self.tmpdirname						,      _UpperCAmelCase  )
									with open(self.image_processor_file						,      '''w'''						,      encoding='''utf-8'''  ) as fp:
													json.dump(_UpperCAmelCase						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self						,      **_UpperCAmelCase  ):
									return CLIPTokenizer.from_pretrained(self.tmpdirname						,      **_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self						,      **_UpperCAmelCase  ):
									return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,      **_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self						,      **_UpperCAmelCase  ):
									return CLIPImageProcessor.from_pretrained(self.tmpdirname						,      **_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									shutil.rmtree(self.tmpdirname  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Union[str, Any]      							=	[np.random.randint(255						,      size=(3, 30, 400)						,      dtype=np.uinta  )]

									__a			:    Any      							=	[Image.fromarray(np.moveaxis(_UpperCAmelCase						,      0						,      -1  )  ) for x in image_inputs]

									return image_inputs







					def 			_lowerCamelCase		(    self  ):
									__a			:    Any      							=	self.get_tokenizer()
									__a			:    Any      							=	self.get_rust_tokenizer()
									__a			:    Optional[Any]      							=	self.get_image_processor()

									__a			:    Optional[int]      							=	CLIPProcessor(tokenizer=_UpperCAmelCase						,      image_processor=_UpperCAmelCase  )
									processor_slow.save_pretrained(self.tmpdirname  )
									__a			:    Optional[int]      							=	CLIPProcessor.from_pretrained(self.tmpdirname						,      use_fast=_UpperCAmelCase  )

									__a			:    List[str]      							=	CLIPProcessor(tokenizer=_UpperCAmelCase						,      image_processor=_UpperCAmelCase  )
									processor_fast.save_pretrained(self.tmpdirname  )
									__a			:    Any      							=	CLIPProcessor.from_pretrained(self.tmpdirname  )

									self.assertEqual(processor_slow.tokenizer.get_vocab()						,      tokenizer_slow.get_vocab()  )
									self.assertEqual(processor_fast.tokenizer.get_vocab()						,      tokenizer_fast.get_vocab()  )
									self.assertEqual(tokenizer_slow.get_vocab()						,      tokenizer_fast.get_vocab()  )
									self.assertIsInstance(processor_slow.tokenizer						,      _UpperCAmelCase  )
									self.assertIsInstance(processor_fast.tokenizer						,      _UpperCAmelCase  )

									self.assertEqual(processor_slow.image_processor.to_json_string()						,      image_processor.to_json_string()  )
									self.assertEqual(processor_fast.image_processor.to_json_string()						,      image_processor.to_json_string()  )
									self.assertIsInstance(processor_slow.image_processor						,      _UpperCAmelCase  )
									self.assertIsInstance(processor_fast.image_processor						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    int      							=	CLIPProcessor(tokenizer=self.get_tokenizer()						,      image_processor=self.get_image_processor()  )
									processor.save_pretrained(self.tmpdirname  )

									__a			:    List[Any]      							=	self.get_tokenizer(bos_token='''(BOS)'''						,      eos_token='''(EOS)'''  )
									__a			:    Optional[Any]      							=	self.get_image_processor(do_normalize=_UpperCAmelCase						,      padding_value=1.0  )

									__a			:    Tuple      							=	CLIPProcessor.from_pretrained(
									    self.tmpdirname						,      bos_token='''(BOS)'''						,      eos_token='''(EOS)'''						,      do_normalize=_UpperCAmelCase						,      padding_value=1.0  )

									self.assertEqual(processor.tokenizer.get_vocab()						,      tokenizer_add_kwargs.get_vocab()  )
									self.assertIsInstance(processor.tokenizer						,      _UpperCAmelCase  )

									self.assertEqual(processor.image_processor.to_json_string()						,      image_processor_add_kwargs.to_json_string()  )
									self.assertIsInstance(processor.image_processor						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Any      							=	self.get_image_processor()
									__a			:    Union[str, Any]      							=	self.get_tokenizer()

									__a			:    Any      							=	CLIPProcessor(tokenizer=_UpperCAmelCase						,      image_processor=_UpperCAmelCase  )

									__a			:    Tuple      							=	self.prepare_image_inputs()

									__a			:    Optional[Any]      							=	image_processor(_UpperCAmelCase						,      return_tensors='''np'''  )
									__a			:    int      							=	processor(images=_UpperCAmelCase						,      return_tensors='''np'''  )

									for key in input_image_proc.keys():
													self.assertAlmostEqual(input_image_proc[key].sum()						,      input_processor[key].sum()						,      delta=1e-2  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[Any]      							=	self.get_image_processor()
									__a			:    Optional[int]      							=	self.get_tokenizer()

									__a			:    Optional[Any]      							=	CLIPProcessor(tokenizer=_UpperCAmelCase						,      image_processor=_UpperCAmelCase  )

									__a			:    Dict      							=	'''lower newer'''

									__a			:    Union[str, Any]      							=	processor(text=_UpperCAmelCase  )

									__a			:    Any      							=	tokenizer(_UpperCAmelCase  )

									for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]						,      encoded_processor[key]  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Any      							=	self.get_image_processor()
									__a			:    List[str]      							=	self.get_tokenizer()

									__a			:    str      							=	CLIPProcessor(tokenizer=_UpperCAmelCase						,      image_processor=_UpperCAmelCase  )

									__a			:    List[Any]      							=	'''lower newer'''
									__a			:    Dict      							=	self.prepare_image_inputs()

									__a			:    List[str]      							=	processor(text=_UpperCAmelCase						,      images=_UpperCAmelCase  )

									self.assertListEqual(list(inputs.keys()  )						,      ['''input_ids''', '''attention_mask''', '''pixel_values''']  )

									# test if it raises when no input is passed
									with pytest.raises(_UpperCAmelCase  ):
													processor()







					def 			_lowerCamelCase		(    self  ):
									__a			:    str      							=	self.get_image_processor()
									__a			:    Optional[int]      							=	self.get_tokenizer()

									__a			:    List[str]      							=	CLIPProcessor(tokenizer=_UpperCAmelCase						,      image_processor=_UpperCAmelCase  )

									__a			:    Optional[int]      							=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									__a			:    Union[str, Any]      							=	processor.batch_decode(_UpperCAmelCase  )
									__a			:    Dict      							=	tokenizer.batch_decode(_UpperCAmelCase  )

									self.assertListEqual(_UpperCAmelCase						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[Any]      							=	self.get_image_processor()
									__a			:    Any      							=	self.get_tokenizer()

									__a			:    str      							=	CLIPProcessor(tokenizer=_UpperCAmelCase						,      image_processor=_UpperCAmelCase  )

									__a			:    Any      							=	'''lower newer'''
									__a			:    Optional[Any]      							=	self.prepare_image_inputs()

									__a			:    Any      							=	processor(text=_UpperCAmelCase						,      images=_UpperCAmelCase  )

									self.assertListEqual(list(inputs.keys()  )						,      processor.model_input_names  )
"""simple docstring"""



import os



def 	_lowerCamelCase(	a = "matrix.txt"				):
       with open(os.path.join(os.path.dirname(a				)   ,    a				)				) as in_file:
              __a       =    in_file.read()

       __a       =    [[int(a				) for cell in row.split(","				)] for row in data.strip().splitlines()]
       __a       =    [[0 for cell in row] for row in grid]
       __a       =    len(grid[0]				)

       __a       =    [[0 for i in range(a				)] for j in range(a				)]
       __a       =    grid[0][0]
       for i in range(1   ,    a				):
              __a       =    grid[0][i] + dp[0][i - 1]
       for i in range(1   ,    a				):
              __a       =    grid[i][0] + dp[i - 1][0]

       for i in range(1   ,    a				):
              for j in range(1   ,    a				):
                     __a       =    grid[i][j] + min(dp[i - 1][j]   ,    dp[i][j - 1]				)

       return dp[-1][-1]


if __name__ == "__main__":
   print(F'''{solution() = }''')
